Nesbitt - Differences in Soil Quality Indicators Between Organic and Sustainably Managed Potato Fields in Canada

8/9/2019 Nesbitt - Differences in Soil Quality Indicators Between Organic and Sustainably Managed Potato Fields in Canada

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Ecological Indicators 37 (2014) 119130

Contents lists available at ScienceDirect

Ecological Indicators

journal homepage: www.elsevier .com/ locate /ecol ind

Differences in soil quality indicators between organic and sustainably

managed potato fields in Eastern Canada

Johanna E. Nesbitt a, Sina M. Adl a,b,

a Department of Biology, Dalhousie University, Halifax, NS,Canada, B3H4J1b Department of Soil Science, University of Saskatchewan, Saskatoon, SK,Canada, S7N5A8

a r t i c l e i n f o

Article history:

Received 2 April 2013Received in revised form 13 August 2013

Accepted 1 October 2013

Keywords:

Agro-ecosystem

Bio-indicators

Farming systems

Organic agriculture

Soil ecology

Soil quality

a b s t r a c t

The aim ofthis study was to determine iforganic management offields promoted soil quality indicators

compared to sustainably managed fields following best-management practice guidelines. Using a soil

quality minimum data set, conventionally and organically managed commercial potato fields in eastern

Canada were compared. Microbial biomass, testate amoebae, nematodes, and microarthropods served as

bioindicators, while soil pH, C:N ratio, light fraction, bulk density, and soil moisture served as the chemical

and physical indicators. We also investigated whether differences in site location (different soil texture

and local climate) were more or less important than field management (organic or conventional). When

site location and seasonal factors were considered, the soil quality indicators were better at differentiating

organic and conventional potato fields. There was no single indicator that could clearly differentiate, on

its own, between the two field managements due to variability with site location or month ofsampling.

Microbial biomass, testate amoebae, microarthropod and soil moisture varied significantly through the

growing season. The mean soil pH, C:N ratio, and moisture were significantly different between sites.

However, the indicators were affected to different degrees, and differed to some extent to both site

location and time ofsampling. The results ofthis study also provide a baseline for similar soil quality

evaluations in management of eastern Canada potato fields. We recommend that several indicators,

including bioindicators should be used together, and that several sites should be sampled. In addition,

one-time field sampling ofan indicator, as it has been often practiced by growers, is likely to give falseresults as it does not account for variability through the growing season.

2013 Elsevier Ltd. All rights reserved.

1. Introduction

Intensification of agriculture was accompanied with increased

use of synthetic fertilizers, pesticidesand herbicides that has raised

concerns regarding their side-effect on the environment. In reac-

tion to this, a variety of sustainable agriculture practices have

gainedpopularity,as well as organic agriculture as an alternative to

the intensive inputs in conventional agriculture. These sustainable

practices include adding organic matter to the soil, covering soil

with cover crops or crop residues,reducingtillage intensityor prac-

ticing conservation tillage, using legumes within a crop rotation,

implementing strip cropping, improving drainage, and avoiding

compaction (Madgoff, 2001; Kennedy and Papendick, 1995). Best

management practices were proposed to reduce the amount of

synthetic chemicals used in conventional agriculture while main-

taining acceptable levels of economic return (Hilliard and Reedyk,

Corresponding author at: Department of Soil Science, University of

Saskatchewan,Saskatoon, SK, Canada, S7N 5A8. Tel.: +1 306966 6866.

E-mail address: [email protected](S.M. Adl).

2000; Korol, 2004). Organic agriculture claims to be environmen-

tally sustainable, socially just, and economically sound production

practices but prohibits using most synthetic fertilizers, herbicides,

and pesticides, as well as other restrictions (Lotter, 2003; El-Hage

Scialabba and Hattam, 2002; Biao et al., 2003). Increasing soil

biological activity in order to maintain long term soil fertility

through decomposition of the organic matter are the first priori-

ties of organic agricultural management practices (IFOAM, 2011;

Fliessbach and Mader, 2000; Biao et al., 2003). In this study we

compared conventional fields under best management practise to

fields under organic agriculture.

Soil quality is a key element in evaluating the sustainability of

agriculture practices (Carter, 2002). By combining Brookes (1993)

criteria, Doran and Parkins (1994) criteria, and Doran and Safleys

(1997) criteria, Stenberg (1999) produced a list of five essential

criteria used in determining proper soil quality indicators. Because

soil functions are difficult to measure, soil properties that are sen-

sitive to change in a specific ecosystem are often used as indicators

of soil quality (Stenberg, 1999; Acton and Padbury, 1993). A min-

imum data set is a group of soil quality indicators that are chosen

based on a definition of soil quality and soil quality indicator

1470-160X/$ see front matter 2013 Elsevier Ltd. All rights reserved.

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120 J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130

criteria (Larson and Pierce, 1991; Doran and Parkin, 1994; Harris

et al., 1996). Modern studies have argued that physical, chemi-

cal, and biological indicators must be evaluated together in order

to provide a correct assessment of soil quality (Stenberg, 1999;

Wander and Bollero, 1999; Stenberg et al., 1998; Gomez et al.,

2004; Schloter et al., 2003a). However, a comprehensive analysis

of soil will not accurately describe soil quality unless the indi-

cators are chosen with a specific soil function in mind, within a

defined ecosystem (Janzen et al., 1992; Stenberg, 1999; Acton and

Padbury, 1993). In this study, we focused on potato production

within the eastern Canada Maritimes and indicators were selected

accordingly.

Potatoes (Solanum tuberosum L.) are the third most important

food crop in the world and play an important role in feeding the

worlds population (International Potato Center, 2011). Potato pro-

ductionineasternCanadacontinuestoincreaseandproducesabout

1,772,000 tonnes annually, and it is the main crop from this region

(Agriculture Canada, 2011). However, potato nutrient demand on

soil is high, andtuberquality requires both high organic matter and

nitrogen availability. The intense use of synthetic chemicals, as fer-

tilizer and pathogen control, in conventional potato production has

also caused concern for the adjacent waterways and the surround-

ing environment (Patriquin et al., 1991). Sustained conventional

potato monoculture as practised is leading to decreased outputper

hectare without substantially increasingchemical inputs, thus rais-

ing costs (Saini andGrant, 1980; Porter andMcBurnie, 1996; Carter

et al., 1998, 2003). To sustain soil fertility and production levels,

more sustainable forms of potato production have been proposed

(i.e.rotations and spring tillage) thatwould reduce production costs

(Porter and McBurnie, 1996; Patriquin et al., 1991; Carter et al.,

1998).

In this study we tested the hypothesis that fields under each

management practice (conventional-best management or organic-

management) would not be different based on indicators of soil

quality. The two management practices were chosen because they

are claimed to be sustainable. Potato fields were chosen because

of the significance of this food crop in the region and globally.

Although other soil quality studies have been conducted on potatofields in Prince Edward Island (Canada) and Maine (U.S.A.) (Carter,

2002; Porter and McBurnie, 1996; Carter et al., 2003), this is the

first study to include bioindicators and to compare fields across the

region. It is also the first study to compare two types of sustainable

practices in potato production in the region.

2. Materials and methods

2.1. Soil sampling

Three conventional and three organic potatofields were chosen

within a 150 km radius for a total of six field sites. All six sites werecommercial farms, not experimental fields. Each organic field was

located within a few kilometres of a conventional field. Fields were

sampled in each of May, July, andSeptember withinone day of each

other. For each field, soil samples were collected at three locations

on a randomly selected diagonal transect across the field. Samples

were taken inthe middleof thepotatohill,approximately1520 cm

from the stem, to a depth of 10 c m. At each sampling location,

separate samples were collected for nematode, testate amoebae,

soil pH, C:N ratio, and soil moisture using a 2.5cm soil corer, as

described below. Similarly, separate samples for microarthropod,

light fraction and bulk density were taken using a 5 cm soil corer,

and microbial biomass was measured from a 1kg composite soil

sample. Soil samples were transported to the lab in a cooler where

they were processed within 24h of sampling.

2.2. Field sites

The first organic management field (O1) is an easilydrained fine

sandy loam Charlottetown series soil, but has a small percentage

of easily drained fine sandy loam Alberry soil as well. The farm has

been under cultivation since the mid 1900s, but was converted to

organic agriculture over a seven year period (19932000) and was

certified in 2000. Parasol 50% (copper hydroxide, fungicide) was

applied twice, Bluestone (copper sulphate, fungicide) was applied

twice, and Entrust (spinosad, insecticide) was applied once during

the 2004 growing season. Potato tops were physically cut off at the

end of the season.

The first conventional management field (C1) is an easily

drained fine sandy loam of the Charlottetown series. The land has

been under potato cultivation since 1942. The potato rows were

seededalongside a bandfertilizer of NPK-Mg. Lorox (linuron, herbi-

cide)was applied once,Manzate(mancozeb, fungicide)was applied

six times, Ridol-Bravo was applied once, Bravo (chlorothalonil,

fungicide) was applied three times, and mineral oil was applied

five times throughout the 2004 growing season. Reglone (diquat

dibromide (37.3%), desiccant/herbicide) was applied twice at the

end of the season as a top kill.

The second organic management field (O2) is a poorly draining

clay loam Washburn series soil. The farm has been in cultivationsince 1980 and was certified organic in 1987. The rotation used in

this field is manure, clover, potatoes, and then mixed vegetables.

Parasol wasapplied three times andEntrust wasapplied twice dur-

ing the 2004 growing season. A propane flamer wasused atthe end

of theseason tocleanup late blight, as a final ColoradoPotato Beetle

control and as a top-kill.

The second conventional management field (C2) is on a poorly

drained silty loam of the Interval series. The land has been under

cultivation since the early 1800s. The rotation includes corn, bras-

sica, and potatoes. Chemical applications to the field did occur but

were not recorded.

Thethirdorganicmanagementfield(O3) is found onlightbrown

sandy loam of the Torbrook series with good to excessive drainage.

The farm has been under organic cultivation since 1988. The rota-tion consists of potatoes, two years of mixed vegetables, followed

by a green manure of oats and peas which are left in through the

winter and harrowed under in the spring. Floating row covers are

used to speed the early stages of growth, and to avoid pests and

disease.

Thethirdconventional management field (C3) is found on sandy

loam Truro series soil with good to fair drainage. Admire (imidac-

loprid, insecticide) and an NPK fertilizer were banded in furrow

when the crop was seeded. An N fertilizer was also broadcast

on the crop mid-growing season. Sencor (herbicide) was applied

once, Bravo was applied three times, and Tatoo C (fungicide) and

Cymbush (cypermethrin, insecticide/miticide) were applied once

throughout the growing season. Reglone was applied once as a top

kill.

2.3. Soil quality indicator measurements

Microbial biomass C was measured using chloroform

fumigation-extraction according to standard procedures (Paul

et al. , 1999). On each sampling occasion, a composite sample of

approximately 1 kg of soil was taken from each field. The soil

was sieved using a 2.83cm diameter sieve and organic particles

larger than 3 m m were removed by hand. The fumigated and

unfumigated extracted filtrates were stored in 50mLfalcon tubes

at 20 C until the chloroform labile C analysis was analysed with

a LECO CNS auto-analyser.

Testate amoebae were stained and enumerated using standard

procedures (Adl et al., 2006a) from three 2.5 cm10cm deep soil


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J.E. Nesbitt, S.M. Adl / Ecological Indicators37 (2014) 119130 121

cores taken at each sampling site of every field (3 samples per

field). One soil composite was obtained from each sampling site

and three subsamples of 1g placed into each of three test tubes.

This resulted in three 1 g subsamples per soil sample for a total of

nine test tubes per field. The slides were dried on a slide dryer and

examined under one drop of glycerine with a cover slide. Three

4cm long transects were observed at 400 on a Zeiss compound

microscope. The number of stained testate amoebae were enumer-

ated along each transect to obtain abundances from line transects

(Krebs, 1998).

Nematodes were extracted and enumerated according

to standard procedures (Coleman et al., 1999) from three

2.5cm10 c m soil cores per sampling location (3 sampling

sites per field) and combined into one composite soil core for

each sampling site. Three subsamples of known weight (48g)

were extracted per sampling location and stored in 5% formalin.

Nematodes were enumerated with a Nikon inverted microscope

at 100 and functional groups were identified at 400.

Microarthropods were extracted and enumerated according to

standard procedure (Coleman et al., 1999). Two 5cm10cm deep

soil cores were taken per field sampling site (3 samples per field),

weighed and placed into an extraction cup. The microarthropods

were collectedin 95% ethanol andenumerated in a Petri dish with a

Nikon dissectingmicroscope. The samples were enumerated at 30

andidentified at 80 to sub-order(mites) or to family (collembola).

Soil pH, light fraction (LF), bulk density (BD) and gravimet-

ric water content were obtained according to Robertson et al.

(1999). The soil C:N ratio was measured according to Elliott et al.

(1999) fromsix compositefield samples that were airdried, ground

to


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May July September

-1000

0

1000

2000

3000

4000

Micro

arthropodAbundance

g-1drysoil

0

2000

4000

6000

Tes

tateAmoebae

g-1drysoil

0

20000

40000

60000

80000

100000

Field and Month

ChloroformLabileC

ugg-1drysoil

0

100

200

300

400

500

May

July

September

Nem

atodeAbundance

g-1drysoil

0

10

20

30

50

100

150

200

250

300

350

20000

30000

40000

50000

60000

70000

Organic

Conventional

Month

0

5

10

15

20

25

O1 C1 O2 C2 O3 C3

A B

C D

E F

G H

Fig. 1. Bioindicators measuredin organically managed fields(O1, O2,O3) and in conventional fields (C1,C2, C3).Pooled dataof all organic (filled circle) or conventional(clear

circle) fields foreach month.

biomass, as implied from chloroform labile carbon, increased over

the growing season in every field (Fig. 1a). Fields under organic

management had the highest chloroform labile C means in the

soil, throughout the growing season, compared to conventional

fields (Fig. 1b). Mean testate amoebae and nematode abundance

was more variable between field sites through the growing season

(Fig. 1c and d). When combining all organic and all conventional

field sites, mean abundance for both decreased over the growing


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Table 2

Nematode functional groupabundance means (with standard error)with respect to theinteraction effects of therandomized block designgeneral linearmodel.

Nematodes(g1) Bacterivores Fungivores Root feeders Omnivores Predators

Management practice

Organic 12 (3) 0 (0) 2 (1) 0 (0) 0 (0)

Conventional 5 (2) 0 (0) 3 (1) 0 (0) 0 (0)

Month

May 10 (3) 0(0) 1 (1) 0 (0) 0 (0)

July 9 (3) 0 (0) 4 (1) 0 (0) 0 (0)

September 6 (3) 2 (1) 2 (1) 0 (0) 0 (0)

Management practicemonth

Organic

May 17 (4) 0 (0) 1 (2) 0 (0) 0 (0)

July 12 (4) 1 (0.2) 2 (2) 0 (0) 0 (0)

September 8 (5) 0 (0) 2 (2) 0 (0) 0 (0)

Conventional

May 4 (4) 0 (0) 2 (2) 0 (0) 0 (0)

July 6 (4) 0 (0) 5 (2) 0 (0) 0 (0)

September 4 (4) 1 (0.3) 3 (2) 0 (0) 0 (0)

Location

A 7 (3) 0 (0) 4 (1) 0 (0) 0 (0)

B 8 (3) 0 (0) 1 (1) 0 (0) 0 (0)

C 11 (3) 1 (0.2) 2 (1) 0 (0) 0 (0)

Means were estimated with Fishers least significant difference withP= 0.05, type III sum of squares.

season (Fig. 1e and f). There was no significant difference in tes-

tateamoebae mean abundances between organic and conventional

fields (Fig. 1d). Mean nematode abundance in the organic fields

was higher than in conventional fields in every month sampled,

but this was not statistically significant. Among the nematode

functional groups, bacterivore, fungivore, root feeder, predator,

and omnivore. Means and standard errors were analysed by ran-

domized block design (Table 2). Nematode functional groups did

not significantly differ between organic and conventional fields,

samplingdate, the interaction between managementand datesam-

pled, or between locations (Tables 2 and 3). Mean microarthropod

abundance was higher in September in most field sites sampled

except in C3 (Fig. 1g), and this was significantly higher for organic

fields (Fig. 1h, Tables 1 and 4). Mean abundance of mite subor-

ders and Collembola families did not significantly differ between

organic and conventional fields, months sampled, interaction

between management practice and month, or between locations

(Table 5).

3.2. Physical and chemical indicators

When organic fields were combined, soil pH was fairly stable

over the growing season, but in the conventional fields the mean

soil pH decreased slightly through the growing season (Fig. 2a and

b, Table 1). The mean C:N ratio was consistently higher in the con-

ventional fields than in the organic fields, but increased over the

growing season in the organic fields (Fig. 2c and d). Light fraction

was measured at the beginning and at the end of the growing sea-

son, in May and September. Mean light fraction weight remained

significantly higher in the organic fields compared to conventional

Table 3

Effect of management practice, month sampled, their interaction, and location on nematode functional groups.

Effect Statistic Value F Error d.f. p-Value Significance

Management practice

Pillais trace 0.754 3.05 5 0.123 NS

Wilks lambda 0.25 3.05 5 0.123 NS

Hotellings trace 3.05 3.05 5 0.123 NS

Roys largest root 3.05 3.05 5 0.123 NS

Month

Pillais trace 1.201 1.78 12 0.170 NS

Wilks lambda 0.06 2.97 10 0.050 *


Roys largest root 10.30 12.36 6 0.004 **


Pillais trace 1.192 1.75 12 0.1777 NS

Wilks lambda 0.09 2.42 10 0.089 NS


Roys largest root 7.08 8.49 6 0.011 *

Location

Pillais trace 0.903 0.99 12 0.498 NS

Wilks lambda 0.26 0.97 10 0.518 NS



Abbreviations and symbols:d.f. (degreesof freedom), typeIII sumof squares wereused, NS (notsignificant), ***p0.001, 1, 2, 3, 4orderof theeffectscontributionto the overall

model.* p0.05.

**

p0.01.


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Field and Month

0.000

0.001

0.002

0.003

0.004

0

2

4

6

8

0

2

4

6

8

10

12

14

16

May

July

September

0.0

0.2

0.4

0.6

0.8

1.0

1.2

1.4

SoilpH

5.0

5.2

5.4

5.6

5.8

6.0

6.2

6.4

C:N

Ratio

7

8

9

10

11

12

Organic

Conventional

Month

May July September

BulkDensity

gcm-3

0.8

0.9

1.0

1.1

LightFraction

g-1drysoil

0.0000

0.0005

0.0010

0.0015

0.0020

0.0025

A B

C D

E F

G H

O1 C1 O2 C2 O3 C3

Fig.2. Physicaland chemicalindicatorsin organicallymanagedfields(O1,O2, O3)and in conventionalfields(C1,C2, C3).Pooleddataof allorganic(filledcircle)or conventional

(clear circle) fields foreach month.

fields, with a slight butnot-significant difference, over the growing

season (Fig. 2e and f). Over the growing season, mean bulk density

decreased from May to July to September in all fields except C2

(Fig. 2g). Conventional sites had a consistently higher mean bulk

density than theorganic fields (Fig.2h). Soil moisture at each samp-

ling time showed the organic fields retained significantly higher

mean soil moisture content than the conventional fields at the time

of sampling (Table 1).


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Table 5

Effect of management practice, month sampled, their interaction, and location on mite and collembolan abundances.

Effect Value F Error d.f.a p-Valueb

Management practice


Wilks lambda 0.02 5.27 1 0.327 NS


Roys largest root 47.42 5.27 1 NS

Month 0.572


Wilks lambda 0.00 2.18 2 NS


Roys largest root 152.45 33.88 2 NS



Wilks lambda 0.00 1.65 2 0.443 NS

Hotellings trace 70.67 0.00 0 NS

Roys largest root 68.02 15.11 2 0.064

Location

Pillais trace 0.284 18.00 4 0.975 NS

Wilks lambda 0.21 18.00 2 0.979 NS

Hotellings trace 0.00 18.00 0 NS


NS, not-significant;1,2,3,4 order of effects contribution to theoverall model;type IIIsum of squares.a

Degrees of freedom.b Significance.

Table 6

Meansand standard errorsof the soil quality indicators with respect to management practice, month, and location,usingthe randomized block generalized linearmodel.

Indicators Microbial biomass

(mg g1)

Testate amoebae

(# g1)

Nematodes

(# g1)

Micro-arthropods

(# g1)

Soil pH C:N ratio Bulk density

(gcm3)

Soil moisture

(%)

Management practice

Organic 242.64 (13.2) 50,100 (5200) 15 (3) 1450 (250) 6.06 (0.1) 9.99 (0.5) 0.905 (0.02) 22.38 (0.8)

Conventional 136.12 (14.7) 47,305 (4700) 9 (3) 660 (230) 5.53 (0.1) 11.46 (0.4) 1.107 (0.01) 15.89 (0.7)

Month

May 135.74 (14.7)a 66,000 (5800) a 12 (4) a 300 (280) a 5.93 (0.1) a 10.17 (0.5) a 1.096 (0.02) a 19.57 (0.9) a

July 202.63 (14.7)b 45,000 (5800)ab 13 (4) a 354 (280) a 5.74 (0.1) a 10.76 (0.5) a 1.066 (0.01) a 16.77 (0.9) b

September 229.77 (16.8) b 35,100 (6600) b 9 (4) a 2510 (320) b 5.72 (0.1) a 11.26 (0.6) a 1.160 (0.02) a 21.07 (1.0) ab

Location

A 171.40 (14.7) a 48,900 (5800) a 11 (4) a 1020 (280) a 5.16 (0.1) a 10.19 (0.5) a 0.984 (0.03) a 15.70 (0.9) a

B 202.00 (14.7) a 44,200 (5800) a 10 (4) a 1200 (280) a 6.40 (0.1) b 12.83 (0.5) b 1.022 (0.03) a 21.52 (0.9) b

C 194.74 (16.8) a 53,000 (6600) a 14 (4) a 950 (320) a 5.82 (0.1) a 9.16 (0.6) a 1.042 (0.05) a 20.19 (1.0) ab

Means estimated using Fishers least significant difference with p= 0.05. Values within the same column labelled with the same letter are not significantly different from

each other, according to Tukeys honestly significant difference calculated atp= 0.05.

Fig. 3. Canonicalcorrespondence analysisbiplot of soil quality indicators, manage-

ment practices and month sampled.

Fig. 4. Canonical correspondence analysis biplot of management practices and

month sampled.


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Table 7

Effect of management practice,monthsampled, their interaction,and location on soil quality,with respect to theinteractioneffects of therandomized block designgeneral

linear model.

Effect Statistic Value F Error d.f. p-Value Significance

Management practice

Pillais trace 0.994 21.01 2 0.046 *

Wilks lambda 0.01 21.01 2 0.046 *

Hotellings trace 84.04 21.01 2 0.046 *


Month

Pillais trace 1.961 20.64 6 0.01 **

Wilks lambda 0.00 15.28 4 0.009 **


Roys largest root 97.49 36.56 3 0.007 **


Pillais trace 1.893 6.44 6 0.015 *

Wilks lambda 0.00 5.33 4 0.059 NS



Location

Pillais trace 1.922 8.93 6 0.006 **

Wilks lambda 0.00 6.45 4 0.042 *



Abbreviations and symbols: d.f. (degrees of freedom), type IIIsum of squares were used, NS (not significant),1,2,3,4

order of theeffects contribution to theoverall model.* p0.05.** p0.01.

Table 8

Effect of management practice, month sampled, their interaction,and location on thesoil quality indicatorlight fraction.

Effect Light fraction (g LF g1 dry soil) F d.f. p-Value Significance

Management practice

Organic 0.00182 (0.0003) 5.77 1 0.061 NS

Conventional 0.00080 (0.0003)

Month

May 0.00137 (0.0003) 0.09 1 0.776 NS

September 0.00124 (0.0003)


Organic May 0.00194 (0.0004) 0.08 1 0.787 NS

September 0.00169 (0.0005)

Conventional May 0.00080 (0.0004)

September 0.00079 (0.0004)

Location

A 0.00246 (0.0003) 7.83 2 0.029 *

B 0.00087 (0.0003)

C 0.0060 (0.004)

Abbreviations and symbols: d.f. (degrees of freedom), type IIIsum of squares were used, NS (not significant).* p0.05.

the smaller the deviation from the grand mean of all the envi-

ronmental variables (Jongman et al., 1995). When the organic and

conventional fields, and May, July and September scores were sep-

arated, the organic, conventional, andJuly scores were shown to be

multicolinear (Table 9 and Fig. 3). When organic and conventionalvariables were combined into a single variable called management

practice, and May, July, and September were combined into a sin-

gle variable called month in CCA biplot B, all multicolinearity was

eliminated(Table9 and Fig.4). The eigenvalues, percentages, cumu-

lative percentages and speciesenvironment correlations of both

biplots are shown in Table 9. Both CCA biplots illustrate the same

correlations and relationships between variables.

Theoccurrenceof testate amoebae close to thecentre of theplotindicates they were not sufficiently affected by management prac-

tise or by any of the other variables to be useful indicators in this

study. The microarthropods indicate they are strongly responsive

Table 9

Canonical correspondence analysis of soil quality indicators, filed site management practices and month sampled.

CCA Biplot, Fig. 3 CCA Biplot, Fig. 4

Axis one Axis two Axis one Axis two

Eigenvalues 0.044 0 0.035 0

Percentage 52.715 0.398 41.833 0.212

Cumulative percentage 52.715 53.113 41.833 42.045

Indicator-environment correlations 0.732 0.505 0.654 0.329

Multicollinearity detected Organic, conventional, July


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Table 10

Intraset correlations between environmental variables and constrained site scores ofFigs. 3 and 4.

CCA Fig.3 CCA Fig. 4

Scores Envi. axis 1 Envi. axis 2 Scores Envi. axis 1 Envi. axis 2

Organic 0.076 0.379 Mngt pract 0.011 0.242

Conventional 0.076 0.379

May 0.436 0.725 Month 0.887 0.231

July 0.226 0.825

September 0.792 0.057

% moisture 0.229 0.193 % moisture 0.155 0.569

Bulk density 0.581 0.159 Bulk density 0.583 0.320

pH 0.195 0.194 pH 0.134 0.502

C:N ratio 0.379 0.263 C:N ratio 0.212 0.305

to the local climate, season and month of sampling, as well as soil

pH, moisture and C:N ratio, but negatively affected by higher bulk

density. The seasonal effect dominates their abundance dynamics

and they are therefore not useful to distinguish between the two

management practices in this study. The microbial biomass esti-

mated from chloroform labile C also responded in the same way as

the microarthropods, but to a lesser extent, and higher biomass

correlated more strongly with organic management. The nema-

todeabundancewas strongly affected by managementpractiseandhigher abundance correlated more with organic management. The

light fraction weightwas higherin organic plots (Table 6 and Fig.2).

The environmental indicators alsorespondedto month of sampling,

showing higher moisture, pH and C:N ratio towards September,

but lower bulk density in September in part caused by potato

tuber mounding. Overall the organic fields correlated with higher

pH, moisture, nematode abundance, micro-arthropod abundance,

microbial biomass, and lower bulk density and C:N ratio. The con-

ventional fields under best management practise correlated with

higher bulk density, higher C:N ratio, lower pH, and lower abun-

dance of bioindicators.

4. Discussion

4.1. Soil management effect on soil quality indicators

Soil quality indicators were meant to be practicaland fast meas-

ures to determine health or fertility levels of soils. When chosen

well, indicators can be used to monitor field sites and to make

management decisions. One needs to exercise caution as there are

studies that exerted huge effortonlyto conclude thewrongbioindi-

cator was selected (Geissen and Kampichler, 2004). The indicators

selected for this study were chosen because they had been pro-

posed as useful indicators in the literature. However, as this study

shows,not allindicatorsare usefulfor thesamepurpose. Some indi-

catorswere betterused forcertain comparisons, butnot others. For

example, some indicators may discriminate between large differ-ences between sites (such as a forest and an agriculture field), but

not smaller differences between similar fields under agriculture. In

addition, the level of taxonomic resolution used in bio-indicators

may be more significant than abundance measures typically used.

Furthermore, our results show that, predictably, indicators fluc-

tuate with month of sampling through the growing season and

with site location. Therefore, for soil quality indicators a one-time

measurement does not suffice, reducing the practicality of such

measures. Using an aggregate of indicators, with sufficient samp-

ling through the growing season, as well as multivariate statistical

analysis of the data provide better indicators of field management

effects on soil. Our results show that our minimum data set of

indicators, when used together, could differentiate between

organic and conventional best-practice potato fields.

When used on their own, the indicators provided mixed results,

some showing no significant difference while others detecting

differences (Table 1): individually, six of the nine soil quality indi-

cators included in the minimum data set differed between organic

and conventional fields. Some indicators responded more to site

location or month than fieldmanagement. For nematode functional

groups, mite suborders, or collembolan family, the abundances

were not significantly different, using a variety of statistical meas-

ures (Tables 25). However, we note not all statistical techniqueswere consistent, so that comparing several is useful. Better dis-

crimination between organic and conventional best-practice fields

was obtained when the fields were averaged by management

(Tables 6 and 7). The indicators detected significant overall differ-

ences between organic and best-practice management, and were

less affected by month of sampling or site location. The multivari-

ate CCA results provided a better visualization of correspondences

among the indicators (Table 10 and Figs. 3 and 4).

4.2. Interactions among indicators andwith management

Overall, the general linear model and the canonical correspon-

dence analysis showed fields under organic management had

higherpH,soilmoisture,litterlightfraction,andlowerC:Nratioandbulk density than the conventional fields. Given the emphasis on

the role of the soil food web in decomposition and mineralization

in organic agriculture, it was expected that organic management

would promote soil conditions that were favourable for biological

activity (Neher, 1999a,b; Fliessbach and Mader, 2000). However,

conventional fields under best management practice were not sig-

nificantly different from the organic fields for testate amoebae

and nematode abundances. The organic fields supported higher

microarthropod abundance but the significant microarthropod

effect is due to the elevated abundance in the organic fields in

September. Only microbial biomass was significantly higher in the

organic fields, and this was probably affected by the lower bulk

density and lower C:N ratio in these fields. The organic field mean

C:N ratio (= 9 .99) and the conventional field mean C:N ratio(= 1 1.46) were below 20:1 (Table 6), which may indicate the

predominance of the bacterial energy channel (Ferris and Matute,

2003). Dendooven et al. (2000) also reported significant differ-

ence between their low conventional and organic soil C:N values

(CON= 8 and ORG= 5). Their study suggests that the low C:N ratio

could be attributed to differences in C availability, to differences in

microbialbiomassC:N ratios, todifferencesin N dynamics,or todif-

ferences that cannot be reflected in the measurements of organic

and conventional practices (Dendooven et al., 2000). Lower bulk

density suggests that the soil in the organic fields was favourable

to biological activity (Harris et al., 1996). When used as a physical

soil quality indicator in comparisons of organic and conventional

soil bulk density, results in other studies have differed. In a field

experiment by Bulluck et al. (2002) organic and synthetic fertility


11/12


amendments were compared. The organic fertilizer, comprised of

cotton-gin trash, compostedyard waste and cattle manure showed

a decrease in bulk density, while the synthetic fertilizer did not

(Bulluck et al., 2002). In a study of three groups of organic and

conventional fields, organic field bulk density was shown to be

significantly higher in the first group, not significantly different in

the second group, and significantly lower in the third group com-

pared to the conventional counterpart (Schjnning et al., 2002).

Schjnning et al. (2002) suggest that their results indicate that

soil compaction due to extensive traffic may reach the same lev-

els in organic fields as is currently experienced in conventional

fields.

Higher microbial biomass and microarthropod abundance in

organic fields may indicate a difference in the quality (C:N ratio)

or availability of plant litter and compost amendments, since both

are strongly related to resource quality and availability (Wardle

and Lavelle, 1997; Wardle et al., 1999). The lower soil C:N ratio

(higher quality)in organic fieldswould support an increased micro-

bial biomass, however microarthropods generally thrive on lower

quality organic matter (higher C:N ratio) (Wardle and Lavelle, 1997;

Georgieva et al., 2005). Microarthropod abundance is known to be

heavily influenced by resource quality andavailability, buthas also

varied with management practice (Behan-Pelletier, 1999, 2003;

Mebes and Filser, 1998; Wardle et al., 1999). In other comparisons

of organic and conventional fields, as in this study, both testate

amoebae and nematode abundances have been shown not to dif-

fer significantly (Foissner, 1997; Neher, 1999b). Testate amoebae

abundance has been cited as a poor indicator of agriculture man-

agement practices differences, while nematode abundances were

expected to differentiate between differing managementpractices,

months and provinces (Georgieva et al., 2005). Our study agrees

with the literature, that the majority of the nematodes found in

organic soils are bacteriovores, and the remaining population is

made upof rootfeeders (Neher, 1999b). Bacteriovores (both nema-

todes and protozoa) have been suggested as better indicators of

bacterial activity, substrate quality, and nutrient release in the

soil than direct measurement of bacterial populations (Georgieva

et al., 2005). If decomposition and nutrient mineralization hadbeen more efficient in the organic systems, indicated by higher

microbial biomass and microarthropod abundance, the abundance

of bacterivorous nematodes and testate amoebae should have

been significantly higher as well. Neher (1999b) suggests that the

nematode communities in organic and conventional fields are too

similar, supporting the view that even with the lack of inorganic

inputs, the organic soil food web composition is not as different

from a conventional soil food web as was once thought. Although

testate amoebae, nematode, and microarthropod abundance are

standard bioindicators that have traditionally been enumerated

(Behan-Pelletier, 1999; Foissner, 1999; Neher, 2001), species rich-

ness and diversity may be more useful indicators of soil quality

and soil health (Naeem, 2002; Adl et al., 2006b). However, it is

most likely that the fields under best-management practice arenot all that different biologically from fields near-by under organic

management.

After discussions with the growers, it became evident that the

organic fields were managed with more frequent tillage than the

fields under best-management practice. We therefore sought a no-

tillage potato field under organic management in the area. We

compared our data to the same quality indicators in the no-till

organic potatoplot forthe monthof September. Theresults suggest

thatwithouttillage,the plots underorganicmanagement haveboth

more functional diversity and higher abundance of indicator orga-

nisms, and are significantly different from the best-management

practice fields (Nesbitt, unpublished). This is consistent with previ-

ous observations on the effect of tillage on soil diversity (Adl et al.,

2006b).

5. Conclusion

Using a combination of indicators that included physical, chem-

ical and biological parameters provided a better evaluation of the

field sites, than if a single or fewer indicators are selected. Indica-

tors varied through the growing season so that multiple sampling

times are necessary to infer any conclusions on field manage-

ment. Bioindicators under best-management-practice were not

that different from the organic management fields. We suggest the

increased tillage frequency in the organic fields is responsible for

preventing a recovery of the biodiversityand organism abundance.

The differences in physical parameters between both management

practices are most likely due to the increased organic matter in the

fields under organic practice. This increased abundance of some

bioindicators but not to the point of being consistently significant.

Acknowledgements

This research was supported by a NSERC grant to S.M.A. We

thank the Dalhousie University Faculty of Graduate Studies, the

School for Resource and Environmental Studies, and the Soil Ecol-

ogySocietyfor their financialsupport andtravel awardsto J.E.N.We

thank the potato growers that co-operated with this study, pro-vided access to fields and information about field management:

Raymond Loo, Lori Robinson, Karen Davidge, Gordon Harvey, Nor-

bert Kungl, Kris Pruski.

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